Assessing labor market transitions in Ghana using panel data

Understanding labor market transitions is important for policymakers and researchers in developing countries. Changes in economic activity status (employed, unemployed, out of labor force) vary significantly from one country to another and also within countries from one socio-economic group to another. Below I summarize analysis from the latest Ghana Annual Household Income and Expenditure Survey (AHIES) published by the Ghana Statistical Service. The AHIES is a panel survey that has followed over 10,000 individuals since 2022Q1 to assess changes in their livelihoods. I am currently analyzing trends and show here what has happened to economic activity between Q2 and Q3. 

Transitions in Ghana labor force status, 2022Q2 - 2022Q3

Source: Own analysis using AHIES from Ghana Statistical Service.

Note: Left hand side is 2022Q2 and right side is 2022Q3

• 81 percent of working age individuals (15-64-years) who were employed in 2022Q2 were still employed in 2022Q3; 7 percent went into unemployment, and 13 percent dropped out of the labor force. Among youth (15-35-years), 72 percent who were employed in 2022Q2 were still employed in 2022Q3, while 9 percent fell into unemployment, and a further 19 percent dropped out of the labor force. The corresponding values for non-youth were 83 percent, 5 percent, and 12 percent.
• 39 percent of working age individuals who were unemployed in 2022Q2 were employed in 2022Q3; 23 percent were still unemployed and 37 percent dropped out of the labor force. Among youth, 27 percent of those who were unemployed in 2022Q2 were still unemployed in 2022Q3, while 34 percent got employment, and a further 40 percent fell out of the labor force. 
• 22 percent of those who were outside the labor force are now employed; 10 percent are unemployed; and 68 percent are still out of the labor force.

One salient observation for a country looking to create jobs for youth is that, compared to non-youth (35 years and older), youth (15-35) are close to 20ppts less likely to transition from unemployment to employment; 10ppts more likely to stay in unemployment, and 5 ppts more likely to transition from unemployment to out-of-labor force. 

Similarly, we can visualize transitions across statuses in employment.

Source: Own analysis using AHIES from Ghana Statistical Service.

Note: Left hand side is 2022Q2 and right side is 2022Q3.

Several factors affect labor market transitions in developing countries. It would be interesting to assess the role of the following: (i) economic cycles; (ii) labor market efficiency; (iii) barriers to returning to work; (iv) sectoral and occupational job mismatches; and, in the longer run, (v) structural transformation. Policymakers and researchers need to understand these factors to design effective labor market policies and services that can help individuals navigate these transitions.

Estimating the returns to education in Ghana

What determines earnings in Ghana? The Human Capital model postulates that the log of earnings of an individual is a function of that individual's productive characteristics. These individual characteristics help explain the marginal productivity and the returns to them.[1] In Mincer (1974) this model was formalized as in equation (1):

In equation (1), lnY_t is the log of earnings in year t, Educ is years of schooling, Exp is years of cumulative work experience, and X is a vector of other variables. We ran this model for Ghana using GLSS data with X including the variables shown in table 1. We build on Gundersen (2016) in specifying the model used in this analysis.[2]

We find that, conditional on age and age squared (as a proxy for experience), sex, parents’ education, occupation, public versus private sector employment, and marital status, an additional year of education boosts annual earnings by 5.7 percent. Experience has a statistically positive marginal effect on annual earnings, but this effect dissipates as experience grows. Being female is associated with poorer labor earnings - the estimated marginal effect of being female on one’s annual earnings is negative and can be interpreted as saying that, conditional on the other correlates, females are expected to earn 74 percent of male earnings per year. If we apply model (1) to explain variation in urban, rural, youth and nonyouth annual earnings, we observe a range of returns of 9 to 14 percent (figure 1). Females show higher conditional marginal returns to education than men; non-youth (ages 35 plus) have greater returns to education than youth (ages 15-35); and the rural/urban difference in returns to education is minimal.

Source: Own estimates using GLSS 7.

Note: The conditional marginal estimates of returns to education are derived from the log-linear model 1, in which we regress the natural logarithm of annual earnings on years of education, age and age squared (as a proxies for experience and experience squared), parents’ education, and marital status. All estimates are statistically significant at the 1 percent level of significance.

Other researchers find that globally (across 131 countries), average rates of return to education are 10.4 percent per year and that the returns are highest in Africa, where estimates average 12.8 percent per year.[3] They also find that in Africa, the highest returns exist at the tertiary level at 21.9 percent. Our estimates confirm this tertiary premium in Ghana - returns for those with 12-plus years of education are close to 20 percent per year (see figure 2). High returns at tertiary levels have remained in the 10-year period assessed, reflecting the relative scarcity of human capital with this level of education.

* p<0.05, ** p<0.01, *** p<0.001

Source: Own estimates based on data from GLSS 7. Note: The conditional marginal estimates of returns to education at each level are derived from a log linear regression of the natural logarithm of annual earnings on age and age squared (as a proxies for experience and experience squared); sex; parents’ education; and marital status. Education splines are generated with STATA’s ‘mkspline’ command, which creates knots specified at 0-6, 9-12, and 12-plus years of schooling, following Gundersen (2016). The average estimates as well as estimates for those with 12 plus years of education are statistically significant in both periods.

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[1] Gundersen, S., 2016. "Disappointing returns to education in Ghana: A test of the robustness of OLS estimates using propensity score matching." International Journal of Educational Development Volume 50, September 2016, Pages 74-89 https://www.sciencedirect.com/science/article/pii/S0738059316300608?via%3Dihub

[2] Mincer, J., 1974. “Schooling, Experience and Earnings.” Columbia University Press

[3] Psacharopoulos & Patrinos, 2018. “Returns to investment in education: a decennial review of the global literature.” Education Economics ISSN: 0964-5292 (Print) 1469-5782 (Online) Journal homepage: https://www.researchgate.net/publication/2528582_Returns_to_Investment_in_Education 

Skill-proximate occupations for non-post-secondary-educated workers in Ghana

In upcoming research, my colleague and I posit that the skill content of a worker’s current occupation is a high dimension piece of information that can function as a job market signal, particularly for low wage, non-post-secondary educated workers. Using the 2013 Skills Towards Employability and Productivity (STEP) Survey data for Ghana, we construct a skill vector for every occupation consisting of a skill measure incorporating routine versus nonroutine; and manual versus cognitive intensity. We then create an occupation relatedness measure across all occupations at the 3-digit ISCO level. The fact that many jobs in different industries share common skills but differ substantially in wages suggests that there may be incomplete information in the labor market and potential pathways for low educated workers to become skilled through alternative routes (STARs). The figure below illustrates the resulting relatedness plot of occupations with node size denoting employment shares and node color representing wages (dark blue being the highest).

Our results show that in 2016/17, there were approximately 1.2 million individuals whose skill profile based on current work is proximate to the skill profile of a higher paying occupation. We call these STARs after Blair et al (2020). Of these, 46 percent were workers with less than post-secondary education in low wage occupations who have skills to transition to a higher wage role in their wage category. Another 344,840 (28.3 percent) were workers with less than post-secondary education in middle wage occupations who have skills to transition to a higher wage role in their wage category. An estimated 292,151 (23.9 percent) were non-post-secondary-educated workers who have skillsets to transition to higher wage work. Finally, there were just 18,379 (1.5 percent) workers with less than post-secondary education who are in high-wage roles.

Shocks and Social Safety Net Program Participation in Ghana

My colleague and I just had our study published. The study discusses the association between household exposure to negative shocks and social safety net program participation in Ghana. To examine this issue, we link data from high-resolution geospatial maps of drought and flood risks to government administrative data on safety net program beneficiaries at the district level. We find that drought risk is positively associated with household participation in selected, main public social safety net programs. (The corresponding evidence for flood risk is weaker). We interpret the finding to be a result of pre-shock program coverage of drought-prone areas, in part achieved indirectly through the intentional targeting of poor areas by the programs.

Structural Transformation and Labor Market Performance in Ghana

I am happy to report that a research paper I have been working on at the World Bank has just been published. The study takes stock of Ghana's economic transformation by decomposing growth drivers and reviewing labor market changes over time. We use three rounds of nationally-representative household surveys as well as macro-level data to benchmark Ghana with structural and aspirational peers.

Ghana's post-independence (1957) industrialization strategy focused on large-scale, state-managed manufacturing industries known as import substitution industrialization (ISI). ISI, accompanied by protectionist measures, aimed to develop domestic production and reduce reliance on imports. During the 1960s, Ghana experienced initial success with ISI, witnessing significant growth in the manufacturing sector, which expanded from 2% to 9% of total output by 1969. 

However, the protective measures led to excess capacity, and policy reversals in the 1970s caused balance of payments crises. Import and interest rate liberalization in 1971 resulted in increased imports and rising debt levels. Production and capacity utilization in import-substituting industries declined from the mid-1970s to 1983. 

Stabilization efforts after 1983, including trade liberalization, exchange rate adjustments, and financial reforms, exposed domestic industries to international competition, leading to increased production costs and cuts. The government implemented tax reforms to boost revenue, and by 1983, Ghana shifted from an ISI strategy to export-led policies supported by the International Monetary Fund (IMF) and World Bank.

Between 1989 and 2010, Ghana's manufacturing sector's contribution to economic growth declined, while the utilities, construction, and mining sectors grew. The services sector consistently remained the largest contributor to overall economic growth during this period. The shift in policy from ISI to export-led strategies had positive effects on economic growth and sectoral dynamics.

A decomposition of growth drivers suggests that labor has been moving from primary activities in rural areas to services activities with low productivity growth in urban areas. As such, the nature of structural change in Ghana has not been growth-enhancing. This finding is consistent with other research on Ghana and Africa, implying a need for an industrial policy that can spur productivity growth in services and so called 'industries without smokestacks.' Further, the traditional marker of industrialization, manufacturing, has fallen as a share of value-added, employment, and exports. Again, Ghana is not unique in seeing what has been referred to 'premature deindustrialization.' 

On the labor market side, Ghana has a high employment rate, low unemployment rate, high shares of self-employment, and low shares of wage employment for its level of development. Subgroup differences show that while women, youth, and rural workers are less likely to be engaged in wage employment, their conditional returns to schooling are higher than for counterpart subgroups. The study also finds an inelastic relationship between GDP growth and employment including wage employment. Building on the World Bank’s Ghana Country Private Sector Diagnostic, the study offers an analysis of feasible sectors to promote based on their employment and productivity-enhancing potential, among other factors. As such, the recommendations that follow strengthen ongoing efforts by the Government to industrialize the economy.